Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis

Hu, Ran; Zhou, Xianghong Jasmine; Li, Wenyuan

doi:10.1089/cmb.2022.0002

Cited by 3 publications

(2 citation statements)

References 83 publications

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“…They absorb the beneficial electrons in our body and create disease. It is also the cause of various chronic diseases [19][20]. Antioxidants have the power to reduce its severity.…”

Section: Introductionmentioning

confidence: 99%

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Logeshwaran¹,

Adhikari

Joshi³

et al. 2022

ijhs

121

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In general, the various medical systems currently available provide insights into changes in the tumor genome of patients with tumor sequencing. Most of the tumor DNA sequencing can also be referred to as genetic specification or genetic testing. The sequence results help clinical decision-making to develop a personalized cancer treatment plan based on the molecular characteristics of the tumor rather than a one-size-fits-all treatment approach. The tumor sequencing also plays a major role in cancer research. In this paper, an improved method based on machine learning was proposed to analyze the sequencing and tumor sequencing patterns of the human gene. This proposed method analyzes the circulatory problems of patients with different tumor types for analysis in the public domain. It also constantly monitors large data sets of cancer or tumor genetic sequences to calculate tumor size and location. This allows the doctor to get an accurate report on the type of tumor and the problems it can cause to the patient. The Analysis of these datasets of cancer tumor gene sequences reveals that the genetic makeup of each patient is different and that no two cancers are the same.

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“…They absorb the beneficial electrons in our body and create disease. It is also the cause of various chronic diseases [19][20]. Antioxidants have the power to reduce its severity.…”

Section: Introductionmentioning

confidence: 99%

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Logeshwaran¹,

Adhikari

Joshi³

et al. 2022

ijhs

121

View full text Add to dashboard Cite

show abstract

“…Moreover, liquid biopsy, as a noninvasive sampling method, can be applied continuously at multiple time points to assist in monitoring disease progression (20).Epigenetics encompasses heritable changes that do not alter the DNA sequence but significantly impact gene function. For instance, alterations in methylation patterns can enhance genomic instability, hindering the expression of tumour-suppressor genes (21)(22)(23). Recent research has demonstrated a clear association between changes in DNA methylation patterns and the development of breast cancer (24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

Value of altered methylation patterns of genes RANBP3, LCP2 and GRAP2 in cfDNA in breast cancer diagnosis

Hu,

Mao,

Lan

et al. 2024

J Med Biochemistry

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Background: The purpose of this study was to investigate the potential of plasma cfDNA methylation patterns in reflecting tumor methylation changes, focusing on three candidate sites, cg02469161, cg11528914, and cg20131654. These sites were selected for verification, with a particular emphasis on their association with breast cancer. Methods: We conducted a comprehensive analysis of 850k whole-methylation sequencing data to identify potential markers for breast cancer detection. Subsequently, we examined the methylation status of genes RANBP3, LCP2, and GRAP2, where the aforementioned sites are located, using cancer and cancer-adjacent tissues from 17 breast cancer patients. We also assessed the methylation patterns in different molecular subtypes and pathological grades of breast cancer. Additionally, we compared the methylation levels of these genes in plasma cfDNA to their performance in tissues. Results: Our analysis revealed that RANBP3, LCP2, and GRAP2 genes exhibited significant methylation differences between cancer and cancer-adjacent tissues. In breast cancer, these genes displayed diagnostic efficiencies of 91.0%, 90.6%, and 92.2%, respectively. Notably, RANBP3 showed a tendency towards lower methylation in HR+ breast cancer, and LCP2 methylation was correlated with tumor malignancy. Importantly, the methylation levels of these three genes in plasma cfDNA closely mirrored their tissue counterparts, with diagnostic efficiencies of 83.3%, 83.9%, and 77.6% for RANBP3, LCP2, and GRAP2, respectively. Conclusions: Our findings suggest that RANBP3, LCP2, and GRAP2 genes, located at the identified methylation sites, can serve as valuable blood molecular markers for the auxiliary diagnosis of breast cancer. This research provides a foundation for further exploration of gene methylation pattern changes in cfDNA for the detection of breast cancer and other cancer types.

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Group-shrinkage feature selection with a spatial network for mining DNA methylation data

Tang

Chang

et al. 2023

Computers in Biology and Medicine

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Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis

Cited by 3 publications

References 83 publications

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Value of altered methylation patterns of genes RANBP3, LCP2 and GRAP2 in cfDNA in breast cancer diagnosis

Group-shrinkage feature selection with a spatial network for mining DNA methylation data

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